3D object reconstruction using photometric mesh representation

ABSTRACT

Techniques are disclosed for 3D object reconstruction using photometric mesh representations. A decoder is pretrained to transform points sampled from 2D patches of representative objects into 3D polygonal meshes. An image frame of the object is fed into an encoder to get an initial latent code vector. For each frame and camera pair from the sequence, a polygonal mesh is rendered at the given viewpoints. The mesh is optimized by creating a virtual viewpoint, rasterized to obtain a depth map. The 3D mesh projections are aligned by projecting the coordinates corresponding to the polygonal face vertices of the rasterized mesh to both selected viewpoints. The photometric error is determined from RGB pixel intensities sampled from both frames. Gradients from the photometric error are backpropagated into the vertices of the assigned polygonal indices by relating the barycentric coordinates of each image to update the latent code vector.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/421,729 filed on May 24, 2019, which is hereby incorporated byreference in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to the field of digital imageprocessing, and more particularly, to techniques for three-dimensional(3D) object reconstruction from image sequences using photometric meshrepresentations.

BACKGROUND

A 3D image of an object can be generated from one or moretwo-dimensional (2D) images using various reconstruction techniques. Forexample, multi-view geometric reconstruction methods, such asstructure-from-motion (SfM) and simultaneous localization and mapping(SLAM), recover point clouds as the underlying 3D structure ofred-green-blue (RGB) image sequences, often with high accuracy. Pointclouds, however, lack inherent 3D spatial structure for efficientreasoning. For this reason, at least in some scenarios, meshrepresentations are more desirable than point clouds for 3D objectreconstruction. A 3D mesh is a structural representation, or model, of apolyhedral object, where the three-dimensional reference points ofpolygons (x, y, and z) in the model define the height, width and depthof various object surfaces. Meshes are significantly more compact asdata structures than point clouds because meshes have inherent geometricstructures defined by point connectivity, while they also representcontinuous surfaces that are useful for many applications, such astexture mapping. However, as will be further explained herein, meshingpoint clouds is a difficult and computationally expensive problem, andexisting solutions for meshing point clouds are impractical.

Another limitation of multi-view geometric methods is that they rely onhand-designed features and can be fragile when assumptions about thosefeatures are invalid for a given image. This happens especially intexture-less regions or with illumination variations. By contrast,data-driven approaches include prior knowledge of shapes that are likelyto be in a given image (also referred to as shape priors) for solvingill-posed 3D reconstruction problems. Such data-driven approaches havebeen applied to 3D prediction tasks using single images. However, theseapproaches can only reliably reconstruct from the known space oftraining examples used for learning, resulting in a limited ability togeneralize to unseen (non-learned) data.

Therefore, complex and non-trivial issues associated with 3D objectreconstruction remain due to the limitations of these existingtechniques.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale.

FIG. 1 shows an example system for 3D object reconstruction usingphotometric mesh representations, in accordance with an embodiment ofthe present disclosure.

FIG. 2A shows an example general overview of the principle of multi-view3D reconstruction using photometric mesh representations, in accordancewith an embodiment of the present disclosure.

FIG. 2B shows an example detailed overview of a technique for 3D objectreconstruction using photometric mesh representations, in accordancewith an embodiment of the present disclosure.

FIGS. 3-5 are flow diagrams of an example process for 3D objectreconstruction using photometric mesh representations, in accordancewith an embodiment of the present disclosure.

FIG. 6 is an example of quantitative results of a process for 3D objectreconstruction, in accordance with an embodiment of the presentdisclosure.

FIG. 7 shows example RGB sequences of objects on top of realistictextured backgrounds.

FIG. 8 shows an example of category specific performance to noise incoordinate system mapping, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Techniques are disclosed for 3D object reconstruction using photometricmesh representations. A photometric loss function augments a deeplearning-based reconstruction system, such as a neural network, whichgenerates 3D meshes directly from images of objects. The photometricloss function enables optimization of the system, so that the resultingoptimized system reconstructs a 3D mesh that is consistent with theimages. The disclosed techniques are particularly beneficial, as theyallow for efficiently predicting scene meshes directly from multi-viewimages, which is different from producing 3D point clouds from theimages and then subsequently meshing those points.

In an embodiment, a decoder, such as a neural network, is pretrained totransform a set of points sampled from 2D patches of representativeobjects into a set of 3D polygonal meshes. Given an image sequence orvideo of an object as an input in the form of RGB images paired withassociated camera matrices, an image frame with the object centered isselected either manually or automatically. The selected frame is fedinto an encoder to get an initial latent code vector as an output. Foreach frame and camera pair from the sequence, a polygonal (e.g.,triangular) mesh is rendered at the given viewpoints through meshrasterization, which produces a rasterized map with polygonal (e.g.,triangular) face indices.

The mesh is optimized by first selecting pairs of consecutive framesfrom the image sequence. Next, for each pair of frames, a virtualviewpoint is created by taking the bisection of the correspondingrotations and camera centers. The mesh is then rasterized to obtain adepth map from the virtual viewpoint. The depth map can be expressed asa set of 3D points that lie on the surface of the mesh. Next, the 2Dmesh projections are aligned by projecting the set of 3D points from thedepth map to both selected viewpoints. The photometric error isdetermined from the difference between the RGB pixel intensities sampledfrom both frames. Thus, a photometric loss function can be computed as apixel-wise intensity error between both synthesized images. Gradientsfrom the photometric error are then backpropagated into the vertices ofthe assigned polygonal (e.g., triangular) indices by relating thebarycentric coordinates of each image to update the latent code vector.Therefore, the pretrained decoder, when optimized over the photometricloss function, can reconstruct a 3D mesh that is consistent with objectsappearing in the sequence of images. With this technique, an initialmesh prediction is optimized by using the RGB information with thecorresponding camera viewpoints. The mesh can be used, for example, as a3D stock asset in a virtual reality (VR) or augmented reality scene whendisplayed as an output to a VR or stereo display. Numerous variationswill be appreciated in light of this disclosure.

General Overview

As previously explained, there are non-trivial problems associated withreconstructing images of 3D objects from 2D images using existingmulti-view geometric methods and data-driven approaches. For instance,while point clouds can recover 3D structures with high accuracy, meshrepresentations are typically more useful than point clouds forinserting objects into an augmented reality (AR) scene and for otherdepth-based effect applications. Furthermore, point cloud-basedrepresentations do not occlude virtual objects behind foreground sceneelements due to noise in the depth and possible holes that appear in thescene. Therefore, 3D point clouds must be converted into a 3D mesh todetermine whether an object is occluded. Nevertheless, meshing pointclouds is a difficult problem, and existing solutions are impractical.

To this end, techniques for 3D object reconstruction using photometricmesh representations are disclosed. More specifically, a mesh ispredicted directly from multi-view images in combination with known 3Dobject models learned by a neural network and other shape priors. Aphotometric consistency loss function augments the deep learning-basedobject-mesh reconstructions. Focusing on object instances for improved3D reconstruction, the disclosed techniques use shape priors, which arepre-defined object shapes used to reconstruct realistic geometry withincomplete observations, and multi-view geometric constraints to refinemesh predictions on the input 2D image sequences. The shape priors canbe obtained, for example, from one or more pretrained neural networks.This approach has the benefit of dense reconstruction with objectsemantics from the learned shape priors, which is not possible from thetraditional pipelines of multi-view stereo (MVS) followed by surfacemeshing. This approach also generalizes to variations in unseen objectsby utilizing multi-view geometry to enforce observation consistencyacross viewpoints.

Mesh reconstruction can be obtained from color (for example, RGB) imagesequences using photometric optimization. Here, this is posed as apiece-wise image alignment problem of individual mesh faces. Gradientsof the photometric error are derived with respect to mesh vertices,allowing the mesh to deform accordingly. An advantage of thisphotometric mesh optimization is its non-reliance on any a priori knowndepth or mask information, which is otherwise a necessary condition forreconstructing objects from image sequences. This permits practicalusage of shape prior-based 3D mesh reconstruction aligned with RGBsequences, such as for applications including robotics (e.g., accuratelocalization for autonomous driving), computer graphics (e.g., physicalsimulation and texture synthesis), and virtual reality.

In more detail, the disclosed techniques incorporate aspects ofmulti-view object reconstruction, mesh reconstruction, shape priorlearning, and image alignment to maximize multi-view photometricconsistency by constraining mesh deformation.

Multi-view calibration and reconstruction are used to estimate cameracoordinates using 2D keypoint matching, a process known as SLAM or SfM,followed by dense reconstruction methods such as MVS and meshing. Other3D reconstruction techniques variously utilize multiple-view consistencybetween 2D mask projections, depth rendering, and general 2Dobservations. These methods all utilize forms of 2D supervision that areeasier to acquire than 3D CAD models, which are relatively limited inquantity. In contrast to existing 3D reconstruction techniques, someembodiments of the present disclosure utilize both geometric andimage-based prior constraints, which overcomes some common multi-viewlimitations, such as missing observations, and texture-less regions.

Meshes can be reconstructed from 3D models (e.g., computer aided designor CAD models), from approximated gradients for 2D mask optimization,and using 2D supervision of textures, masks, and 2D keypoints. Incontrast to existing mesh reconstruction techniques, some embodiments ofthe present disclosure use photometric cues across image viewpointsrather than relying on masks or keypoints. Furthermore, some embodimentsof the present disclosure are optimized for 3D meshes using 2Dphotometric cues. A larger amount of perturbation noise can be accountedfor by optimizing over a latent feature vector, or shape code, insteadof over mesh vertices, making such embodiments more suitable forpractical uses.

A plane and primitive prior have been used for the challenging task ofmulti-view scene reconstruction. Although a geometric primitive priordoes not need to learn from an object dataset, the resultingreconstructions can differ significantly from the target geometry whenthe object is not well represented by the chosen primitives. Pretrainedneural networks representing shape priors, in combination with pointclouds, can be used instead of primitives to improve 3D reconstruction.Typically, with neural networks, object masks are needed as additionalconstraints on the input images during optimization to isolate theobjects from the background. However, generating accurate object masksis a non-trivial task. By contrast, some embodiments of the presentdisclosure eliminate the need for mask supervision by regularizing theoptimized latent feature vectors from the pretrained neural network toensure that the feature vectors remain within a trusted region of thefeature space and to prevent the meshes from falling to degeneratesolutions. Previously, shape priors have also been utilized for findingshape correspondences, where the network learns the deformation fieldfrom a template shape to match 3D observations. By contrast, someembodiments of the present disclosure instead directly optimize thelatent feature vectors to match 2D cues from multiple viewpoints, and donot require a known shape template for the object.

For image alignment, mesh optimization is posed as multiple imagealignment problems of mesh faces, which is solved by optimizing a latentfeature vector from the encoder of a deep network, rather than thevertices themselves.

System Architecture

FIG. 1 shows an example system 100 for three-dimensional (3D) objectreconstruction using photometric mesh representations, in accordancewith an embodiment of the present disclosure. The system 100 includes acomputing device 110 having a processor 120, an Object ReconstructionApplication 130, and a graphical user interface (GUI) 140. The GUI 140includes a display and user input device. The processor 120 of thecomputing device 110 is configured to execute the following modules,each of which is described in further detail below: Mesh GenerationModule 150, Mesh Optimization Module 152, and Training Module 154. Thecomputing device 110 is further configured to receive, as inputs, atleast two images of an object and data representing shape priors 112,and an object mesh generation neural network 114. The network 114provides, to the computing device 110, object models representing shapepriors learned by the network using machine learning techniques. Theimages 112 represent a series of images or a sequence of video frames ofthe object taken from differing viewing angles to be processed by thesystem 100. The computing device 110 is further configured to produce,as an output, a reconstructed representation 116 of the object that isbased at least in part on the object images and shape priors 112 and theobject mesh generation neural network 114. The reconstructedrepresentation 116 can, for example, include a digital image or a seriesof digital images that virtually replicates, in 3D, the object appearingin the images 112, or data that can be used to physically replicate theobject in 3D, such as via a 3D printer, such as variously described inthis disclosure. Any number of standard or proprietary digital images(e.g., JPEG, bitmap, PNG, TIFF, QuickTime VR, and PANO) can be used forthe object images 112 and images generated from the reconstructedrepresentation of the object 116. Each of the modules 150, 152, and 154can be used in conjunction with each other for 3D object reconstructionusing photometric mesh representations, with the reconstruction processproducing the reconstructed representation 116 or other data associatedwith the reconstructed representation 116, such as a structuralrepresentation (e.g., a polygon mesh) of the object that can be used togenerate one or more visual images of the object in 3D or 3D printingsof the object.

The computing device 110 may be any computer system, such as aworkstation, desktop computer, server, laptop, handheld computer, tabletcomputer (e.g., the iPad® tablet computer), mobile computing orcommunication device (e.g., the iPhone® mobile communication device, theAndroid™ mobile communication device, and the like), VR device or VRcomponent (e.g., headset, hand glove, camera, treadmill, etc.) or otherform of computing or telecommunications device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described in this disclosure. Adistributed computational system may be provided including a pluralityof such computing devices.

The computing device 110 includes one or more storage devices 122 ornon-transitory computer-readable media 124 having encoded thereon one ormore computer-executable instructions or software for implementingtechniques as variously described in this disclosure. The storagedevices 122 may include a computer system memory or random accessmemory, such as a durable disk storage (which may include any suitableoptical or magnetic durable storage device, e.g., RAM, ROM, Flash, USBdrive, or other semiconductor-based storage medium), a hard-drive,CD-ROM, or other computer readable media, for storing data andcomputer-readable instructions or software that implement variousembodiments as taught in this disclosure. The storage device 122 mayinclude other types of memory as well, or combinations thereof. Thestorage device 122 may be provided on the computing device 110 orprovided separately or remotely from the computing device 110. Thenon-transitory computer-readable media 124 may include, but are notlimited to, one or more types of hardware memory, non-transitorytangible media (for example, one or more magnetic storage disks, one ormore optical disks, one or more USB flash drives), and the like. Thenon-transitory computer-readable media 124 included in the computingdevice 110 may store computer-readable and computer-executableinstructions or software for implementing various embodiments. Thecomputer-readable media 124 may be provided on the computing device 110or provided separately or remotely from the computing device 110.

The computing device 110 also includes at least one processor 120 forexecuting computer-readable and computer-executable instructions orsoftware stored in the storage device 122 or non-transitorycomputer-readable media 124 and other programs for controlling systemhardware. Virtualization may be employed in the computing device 110 sothat infrastructure and resources in the computing device 110 may beshared dynamically. For example, a virtual machine may be provided tohandle a process running on multiple processors so that the processappears to be using only one computing resource rather than multiplecomputing resources. Multiple virtual machines may also be used with oneprocessor. Network interface (I/F) 126 can be any appropriate networkchip or chipset which allows for wired or wireless connection betweenthe device 110 and a communication network (not shown) and othercomputing devices and resources.

A user may interact with the computing device 110 through an outputdevice 160, such as a screen or monitor, including an augmented realitydisplay device, which may display one or more user interfaces providedin accordance with some embodiments. The output device 160 may alsodisplay other aspects, elements or information or data associated withsome embodiments. The computing device 110 may include input orinput/output devices 162 for receiving input from a user, for example, akeyboard, a joystick, a game controller, a pointing device (e.g., amouse, a user's finger interfacing directly with a touch-sensitivedisplay device, etc.), or any suitable user interface, including an ARheadset. The computing device 110 may include other suitableconventional I/O peripherals. The computing device 110 includes or isoperatively coupled to various suitable devices for performing one ormore of the aspects as variously described in this disclosure.

The computing device 110 may run any operating system, such as any ofthe versions of Microsoft® Windows® operating systems, the differentreleases of the Unix® and Linux® operating systems, any version of theMacOS® for Macintosh computers, any embedded operating system, anyreal-time operating system, any open source operating system, anyproprietary operating system, any operating systems for mobile computingdevices, or any other operating system capable of running on thecomputing device 110 and performing the operations described in thisdisclosure. In an embodiment, the operating system may be run on one ormore cloud machine instances.

In other embodiments, the functional components/modules may beimplemented with hardware, such as gate level logic (e.g., FPGA) or apurpose-built semiconductor (e.g., ASIC). Still other embodiments may beimplemented with a microcontroller having several input/output ports forreceiving and outputting data, and several embedded routines forcarrying out the functionality described in this disclosure. In a moregeneral sense, any suitable combination of hardware, software, andfirmware can be used, as will be apparent.

As will be appreciated in light of this disclosure, the various modulesand components of the system, such as the modules 150, 152, 154, the GUI140, or any combination of these, is implemented in software, such as aset of instructions (e.g., HTML, XML, C, C++, object-oriented C,JavaScript®, Java®, BASIC, etc.) encoded on any computer readable mediumor computer program product (e.g., hard drive, server, disc, or othersuitable non-transitory memory or set of memories), that when executedby one or more processors, cause the various methodologies provided inthis disclosure to be carried out. It will be appreciated that, in someembodiments, various functions and data transformations performed by theuser computing system, as described in this disclosure, can be performedby similar processors or databases in different configurations andarrangements, and that the depicted embodiments are not intended to belimiting. Various components of this example embodiment, including thecomputing device 100, may be integrated into, for example, one or moredesktop or laptop computers, workstations, tablets, smart phones, gameconsoles, set-top boxes, or other such computing devices. Othercomponentry and modules typical of a computing system, such asprocessors (e.g., central processing unit and co-processor, graphicsprocessor, etc.), input devices (e.g., keyboard, mouse, touch pad, touchscreen, etc.), and operating system, are not shown but will be readilyapparent.

Methodology

FIG. 2A shows an overview of the principle of multi-view 3Dreconstruction. A scene 200 includes an object 202. Two or more cameras204 a, 204 b obtain images of a point 206 a on the surface of the object202 from different viewing angles. Each camera 204 a, 204 b has anassociated reference frame 208 a, 208 b onto which the point 206 a isprojected, indicated at 206 b and 206 c. Given the multiple projections206 b, 206 c of the same point 206 a onto the multiple images, a 3Dposition of the point 206 a is the intersection of the two projectionrays, a technique referred to as triangulation. The 3D position of thepoint 206 a can be triangulated from the known locations andorientations of the cameras 204 a, 204 b relative to the object 202.However, finding the correspondence between the projections 206 b and206 c, which are needed to identify the 3D position of the point 206 a,is a difficult problem.

FIG. 2B shows an example overview 250 of a technique for 3D objectreconstruction using photometric mesh representations that optimize forobject meshes while maximizing multi-view photometric consistency, inaccordance with an embodiment of the present disclosure.Three-dimensional object reconstruction is posed as a piece-wise imagealignment problem in which the mesh deformation is constrained over ashape prior parametrized by a neural network. The disclosed techniquesutilize the pretrained object mesh generation network 114 as the shapeprior, and camera matrices are obtained from the object image sequence112 using SfM methods. Triangular meshes can be used, although thedisclosed techniques are applicable to any polygonal mesh type, as willbe appreciated in view of this disclosure.

Piece-Wise Image Alignment

Dense 2D projections from a 3D mesh of an object 254, and thus thepolygonal faces 258 of the mesh 254, are all presumed to be consistentacross camera viewpoints 256 a, 256 b, 256 c. Therefore, the problem of3D mesh alignment becomes a collection of piece-wise 2D image alignmentsubproblems of each triangular face 258 for each projection. For atriangular mesh with N vertices, denoted as a set of vertices V∈

^(N×3), a photometric objective function

_(phot) can be decomposed as:

$\begin{matrix}{{{\mathcal{L}_{phot}(V)} = {\sum\limits_{j}{\mathcal{L}_{phot}^{(j)}\left( V_{j} \right)}}},} & (1)\end{matrix}$where

^((j)) _(phot) is part of the photometric loss contributed by triangle jwith its vertices written as V_(j)∈

^(3x3), tied together by a predefined mesh topology if shared bymultiple faces.

2D image alignment can be achieved by solving for a parameterized warp

(⋅) on a source image I_(S) against a template image I_(T). According toan embodiment, this solution can be written as a per-trianglephotometric objective function:

$\begin{matrix}{{{\mathcal{L}_{phot}^{(j)}\left( V_{j} \right)} = {\sum\limits_{{i\text{:}x_{i}} \in \mathcal{X}_{j}}{{{\mathcal{I}_{S}\left( x_{i}^{\prime} \right)} - {\mathcal{I}_{T}\left( x_{i} \right)}}}_{1}}},} & (2)\end{matrix}$where x′_(i)=

(x_(i); V_(j)) is the warped pixel coordinate where I_(S) is sampled at,and χ_(j) is the set of visible pixel coordinates within the projectionrange of triangular face j. The warp function

(⋅), parameterized by the 3D triangle vertices V_(j), are aback-projection π⁻¹(⋅;⋅,Ω_(T)) from the template view onto triangle jcomposed with a reprojection π(⋅;Ω_(S)) onto the source view, governedby camera matrices Ω_(T) and Ω_(S), respectively. We can thus rewritex′_(i) asx′ _(i)=π(π⁻¹(x _(i) ;V _(j),Ω_(T));Ω_(S))∀i:x _(i)∈χ_(j)  (3)

Back-projection π⁻¹(⋅) typically requires depth to be known, such as insome SLAM problems, but in accordance with an embodiment,back-projection can be directly solved through ray-triangle intersectionfor a given V_(j). In the case where multiple triangles intersect, theclosest triangles are selected using mesh rendering techniques such asrasterization or ray-tracing. This determines which vertices thephotometric gradients from each pixel should contribute andbackpropagate to. This also retains true differentiability without theneed to resort to approximate gradients.

Mesh Alignment Using Virtual Templates

In accordance with an embodiment, χ_(j) represents the visible pixelcoordinates inside the projection of triangle j (for example, triangle258). When aligning mesh projections, χ_(j) varies with the trianglevertices V_(j). This is unlike existing image alignment techniques wherethe template coordinates χ are constant, and warps are asymmetricallyapplied only to the source image. To account for simultaneousvariability of paired imaging with respect to V_(j), image gradientsfrom both images are used to maintain stability during optimization.

The photometric objective can be reformulated to:

$\begin{matrix}{{{\mathcal{L}_{phot}^{(j)}\left( V_{j} \right)} = {\sum\limits_{{i\text{:}x_{i}} \in \mathcal{X}_{j}}{{{\mathcal{I}_{1}\left( x_{i}^{\prime} \right)} - {\mathcal{I}_{2}\left( x_{i}^{''} \right)}}}_{1}}},} & (4) \\{{{{where}\mspace{14mu} x_{i}^{\prime}} = {\pi\left( {{\pi^{- 1}\left( {{x_{i};V_{j}},\Omega_{VT}} \right)};\Omega_{1}} \right)}},} & \; \\{x_{i}^{''} = {{\pi\left( {{\pi^{- 1}\left( {{x_{i};V_{j}},\Omega_{VT}} \right)};\Omega_{2}} \right)}.}} & \;\end{matrix}$

Here, Ω_(VT) is the camera matrix at a virtual template view. Virtualtemplates are used because the reprojection of x_(i) in Equation (3)back to itself can be written as:x′ _(i)=π(π⁻¹(x _(i) ;V _(j),Ω_(T));Ω_(T))=x _(i) ∀x _(i)  (5)

By re-projecting the coordinates from a third frame, virtual templatesallow correct gradient computation

$\frac{\partial\mathcal{I}}{\partial V_{j}} = {\frac{\partial\mathcal{I}}{\partial x_{i}^{\prime}}\frac{\partial x_{i}^{\prime}}{\partial V_{j}}}$from both images, where

$\frac{\partial\mathcal{I}}{\partial x_{i}^{\prime}}$can be obtained through differentiable image sampling. In accordancewith an embodiment, Ω_(T) is chosen to be the bisection between Ω₁ andΩ₂, although it will be understood that Ω_(T) can be arbitrarily chosen.

Mesh Optimization

Optimizing for a 3D mesh with N vertices involves solving for 3N degreesof freedom (DoFs), which typically becomes an under-constrained problemwhen N is large. Therefore, regularization is used to ensure object meshdeformations are well-behaved during optimization. According to anembodiment, deep neural networks can be used as shape priors andoptimize over a latent feature vector, or shape code, z. The vertices Vcan thus be re-parameterized as V=g(z), where g represents a neuralnetwork. This has the advantage of allowing the mesh to deform within alearned shape space, while avoiding the many local minima that existwith direct vertex optimization.

Mesh predictions from neural networks lie in a canonical coordinatesystem independent of the world coordinate system recovered by SfM. Acoarse alignment of these coordinate systems can be computed from cheapannotation of rough correspondences. To more accurately align the meshesto the RGB sequences, a 3D similarity transform refinement

(⋅) on the mesh vertices V=[v₁, v₂, . . . , V_(N)]^(τ) can be optimizedas:v′ _(i)=

(v _(i);θ)=exp(s)

(ω)v _(i) +t∀i,  (6)where θ=[s, ω, t]^(τ)∈

⁷ are the 7-DoF parameters and R is a 3D rotation matrix parameterizedby ω. The exponential on s is taken to ensure positivity. Thisparameterization is used to place extra constraints (e.g., scale).

Despite neural networks being effective priors, the latent featurevector space is reasonable only within the span captured by the trainingdata. To avoid object meshes from falling to degenerate solutions, anextra penalty is imposed on the code z to ensure it stays within a trustregion of the initial code z₀ (extracted from a pretrained image encoderf), defined as:

_(code) =∥z−z ₀∥₂ ².

Additionally, a scale penalty

_(scale)=−s is added to encourage the mesh to expand, since the meshshrinking to infinitesimal is a trivial solution with zero photometricerror.

The full optimization loss function can be represented by:

$\begin{matrix}{{{\min\limits_{z,\theta}\mspace{14mu}{\mathcal{L}_{phot}\left( {z,\theta} \right)}} + {\lambda_{code} \cdot {\mathcal{L}_{code}(z)}} + {\lambda_{scale} \cdot {\mathcal{L}_{scale}(\theta)}}},} & (7)\end{matrix}$where λ_(code) and λ_(scale) are the penalty weights of the additionalregularization. All functions are fully differentiable with respect tothe photometric error.

Example Method

FIGS. 3-5 show flow diagrams of an example process 300 for 3D objectreconstruction using photometric mesh representations, in accordancewith an embodiment of the present disclosure. The process can beimplemented, for example, in the image reconstruction application 130 ofFIG. 1. The process 300 includes generating (302) a polygonal mesh 304representing a shape of a first object in three dimensions. The meshgeneration module 150 of FIG. 1 can, for example, be configured togenerate the polygonal mesh 304. The mesh is generated using the objectmesh generation neural network 114. The neural network 114 is trained totransform a set of two-dimensional (2D) data points 306 representing thefirst object into the polygon mesh. The set of 2D data points 306represents color (RGB) pixels in at least two images of the firstobject. The images can be a series or sequence of still images, orframes of a video. The images have different camera poses in that theimages provide differing viewing angles of the object, such as front,side, top, and so forth.

FIG. 4 shows a flow diagram of the process for generating (302) thepolygonal mesh 304 in further detail. Initially, an image frame with theobject centered is selected (402) from the image sequence or video inthe form of RGB images paired with associated camera matrices. The imageframe can be selected either manually or automatically. The selectedframe is fed (404) into an encoder to get an initial latent code vector406 as an output. For each frame and camera pair from the sequence, apolygonal (e.g., triangular) mesh is rendered (408) at the givenviewpoints through mesh rasterization, which produces a rasterized mapwith polygonal (e.g., triangular) face indices (polygonal mesh 304).

Referring again to FIG. 3, the method 300 further includes optimizing(308) the polygonal mesh 304 over a set of latent feature vectors, or acode, using a photometric objective function. The mesh optimizationmodule 152 of FIG. 1 can, for example, be configured to optimize thepolygonal mesh. The set of latent feature vectors represent one or morepre-defined shapes of a second object, also referred to in thisdisclosure as a shape prior. In some cases, the second object is a modelof the first object. For example, if the first object (the object in theimages) is an office chair, the second object can be a structural modelof the same type of office chair or a similarly shaped chair developedfrom a large set of previously validated data representing such chairs,as will be understood by one of skill in the art of computer vision anddeep learning networks.

FIG. 5 shows a flow diagram of the process for optimizing (308) thepolygonal mesh 304 in further detail. The mesh is optimized by firstselecting (502) pairs of consecutive frames 504 from the image sequence.Next, for each pair of frames 504, a virtual viewpoint 508 is created(506) by taking the bisection of the corresponding rotations and cameracenters. The mesh is then rasterized (510) to obtain a depth map fromthe virtual viewpoint. The depth map can be expressed as a set of 3Dpoints that lie on the surface of the mesh. Next, the 3D meshprojections are aligned (514) by projecting the set of 3D points fromthe depth map to both selected viewpoints, from which pixel intensities516 can be sampled. The photometric error is determined (518) from thedifference between the RGB pixel intensities 516 sampled from bothframes. Thus, a photometric objective function can be computed as apixel-wise intensity error between both synthesized images. As describedabove, the photometric objective function represents, at least in part,a photometric loss contributed by pixels in each respective face of themesh as a function of the image gradients obtained from the color (RGB)pixels. Gradients from the photometric error are then backpropagated(522) into the vertices of the assigned polygonal (e.g., triangular)indices by relating the barycentric coordinates of each image to updatethe latent code vector, thereby producing the reconstructedrepresentation of the object 310 (reconstructed mesh). In some cases,the photometric objective function includes applying a parameterizedwarp function to pixels in the at least two images of the first object,such as discussed above. In some cases, the process 300 further includescausing 314 the reconstructed image of the first object to be displayedvia a display device, for example, via the GUI 140 of FIG. 1. In somecases, the reconstructed image of the first object can be used to printa physical representation of the object using a 3D printer or othersuitable device for forming a specimen of the object out of a physicalmaterial.

Referring again to FIG. 3, in some cases, the process 300 furtherincludes training 316, by the at least one processor, the object meshgeneration network to transform the set of 2D data points into thepolygon mesh using 3D computer aided drawing (CAD) model renderings. Thetraining module 154 of FIG. 1 can, for example, be configured to trainthe network. Such training is useful, for example, when additionalvalidated shape priors are available to improve performance of thenetwork for a given class of objects (for instance, for learning a newobject).

FIG. 6 is an example of quantitative results using the process 300,showing a sequence of RGB images 602 and the corresponding reconstructedimages 604. The disclosed techniques can take advantage of multi-viewgeometry to resolve large misalignments and optimize for more accurateshapes. The high photometric error from the background between viewsdiscourages mesh vertices from staying in such regions. This serves as anatural force to constrain the mesh within the desired 3D regions,eliminating the need of additional depth or mask information duringoptimization.

The disclosed techniques can be applied to both single and multipleobject categories of image sequences, using synthetic data as well asreal-world videos. Datasets of 3D CAD model renderings are generated fortraining a mesh generation network as well as for evaluating theoptimization framework. The rendering pipeline aims to generaterealistic images with complex backgrounds so then can be applied toreal-world video sequences. A predefined object dataset can be used, andall objects are normalized to fit an origin-centered unit sphere. RGBimages of each object are rendered using perspective cameras at, forexample, 24 equally spaced azimuth angles and 3 elevation angles. Tosimulate more realistic backgrounds, spherical images from a databaseare randomly warped and cropped to create background images of the samescene taken at different camera viewpoints. By compositing theforeground and background images together at corresponding camera poses,RGB sequences of objects on top of realistic textured backgrounds areobtained, such as shown in FIG. 7.

During optimization, θ is initialized to 0 (identity transform), wherethe rotation component ω is parameterized with the so(3) Lie algebra.The code z₀ is initialized by encoding an RGB frame with the encoder:for synthetic sequences, frames at azimuth angle 45° are used; forreal-world sequences, a frame is selected where the object iscenter-aligned to the image as much as possible to match the renderingsettings. During optimization, two consecutive frames are selected asthe image pair and a stochastic strategy of randomly selecting 8 pairsper iteration is used.

FIG. 8 shows examples of category specific performance to noise incoordinate system mapping when applying the disclosed techniques to theinput image sequences.

Numerous embodiments will be apparent in light of the presentdisclosure, and features described herein can be combined in any numberof configurations. One example embodiment provides, in a digital mediumenvironment for editing digital images, a computer-implemented method ofthree-dimensional object reconstruction. The method includes generating,by at least one processor, a polygon mesh representing a shape of afirst object in three dimensions using an object mesh generation neuralnetwork trained to transform a set of two-dimensional (2D) data pointsrepresenting the first object into the polygon mesh. The set of 2D datapoints represent color pixels in at least two images of the firstobject. The at least two images have different camera poses. The methodfurther includes optimizing, by the at least one processor, the polygonmesh over a set of latent feature vectors using a photometric objectivefunction to produce a reconstructed representation of the first object,the set of latent feature vectors representing a pre-defined shape of asecond object, and causing, by the at least one processor, thereconstructed representation of the first object to be output to anoutput device. In some cases, the method includes training, by the atleast one processor, the object mesh generation network to transform theset of 2D data points into the polygon mesh using 3D computer aideddrawing (CAD) model renderings. In some cases, the second object is amodel of the first object. In some cases, the photometric objectivefunction represents, at least in part, a photometric loss contributed bypixels in the at least one face of the polygon mesh. In some cases, thephotometric objective function includes applying a parameterized warpfunction to pixels in the at least two images of the first object. Insome cases, the photometric objective function is:

${\mathcal{L}_{phot}^{(j)}\left( V_{j} \right)} = {\sum\limits_{{i\text{:}x_{i}} \in \mathcal{X}_{j}}{{{\mathcal{I}_{S}\left( x_{i}^{\prime} \right)} - {\mathcal{I}_{T}\left( x_{i} \right)}}}_{1}}$

where x′_(i)=

(x_(i); V_(j)) is a warped pixel coordinate x_(i) in a polygon mesh V ofa first image I_(S) of the first object, I_(T) is a second image of thefirst object, V is the polygon mesh, and χ_(j) is a set of visible pixelcoordinates within a projection range of the at least one face j of thepolygon mesh. In some cases, the optimizing includes applying a scalepenalty to the optimization loss function. In some cases, the outputdevice includes at least one of a display device for displaying thereconstructed representation of the first object and a 3D printer devicefor 3D printing of the reconstructed representation of the first object.In some cases, the output device includes a memory device for storingthe reconstructed representation of the first object. Another exampleembodiment provides a computer program product including one or morenon-transitory machine-readable mediums having instructions encodedthereon that when executed by one or more processors cause the one ormore computer processors to perform a process such as set forth in thisparagraph.

Another example embodiments provides a system for 3D objectreconstruction using photometric mesh representations. The systemincludes at least one processor, and a storage operatively coupled tothe at least one processor and for storing instructions that whenexecuted by the at least one processor cause the at least one processorto generate a polygon mesh representing a shape of a first object inthree dimensions using an object mesh generation neural network trainedto transform a set of two-dimensional (2D) data points representing thefirst object into the polygon mesh. The set of 2D data points representscolor pixels in at least two images of the first object. The at leasttwo images have different camera poses. In some cases, the instructionscause the at least one processor to optimize the polygon mesh over a setof latent feature vectors using a photometric objective function toproduce a reconstructed representation of the first object, the set oflatent feature vectors representing a pre-defined shape of a secondobject, and cause the reconstructed representation of the first objectto be at least one of displayed via a display device and printed via a3D printing device. In some cases, the second object is a model of thefirst object. In some cases, the photometric objective functionrepresents, at least in part, a photometric loss contributed by pixelsin the at least one face of the polygon mesh. In some cases, thephotometric objective function includes applying a parameterized warpfunction to pixels in the at least two images of the first object. Insome cases, the photometric objective function is:

${\mathcal{L}_{phot}^{(j)}\left( V_{j} \right)} = {\sum\limits_{{i\text{:}x_{i}} \in \mathcal{X}_{j}}{{{\mathcal{I}_{S}\left( x_{i}^{\prime} \right)} - {\mathcal{I}_{T}\left( x_{i} \right)}}}_{1}}$where x′_(i)=

(x_(i); V_(j)) is a warped pixel coordinate x_(i) in a polygon mesh V ofa first image I_(S) of the first object, I_(T) is a second image of thefirst object, V is the polygon mesh, and χ_(j) is a set of visible pixelcoordinates within a projection range of the at least one face j of thepolygon mesh. In some cases, the optimizing includes applying a scalepenalty to the optimization loss function.

The foregoing description and drawings of various embodiments arepresented by way of example only. These examples are not intended to beexhaustive or to limit the invention to the precise forms disclosed.Alterations, modifications, and variations will be apparent in light ofthis disclosure and are intended to be within the scope of the inventionas set forth in the claims.

What is claimed is:
 1. In a digital medium environment for editingdigital images, a computer-implemented method of three-dimensionalobject reconstruction, the method comprising: transforming a set oftwo-dimensional (2D) data points representing one or more shape priorsinto a first set of one or more latent feature vectors representing ashape of a first object; generating a reconstructed representation ofthe first object in three dimensions based on the first set of latentfeature vectors and a second set of latent feature vectors representinga pre-defined shape of a second object; and causing the reconstructedrepresentation of the first object to be output to an output device. 2.The method of claim 1, further comprising generating a polygonal meshrepresenting the shape of the first object using an object meshgeneration neural network trained to transform the set of 2D data pointsrepresenting the first object into the polygonal mesh.
 3. The method ofclaim 2, wherein generating the polygonal mesh includes: selecting animage of the first object from an image sequence; generating the firstset of latent feature vectors by feeding the selected image of the firstobject into an encoder of the neural network; and rendering thepolygonal mesh based on the first set of latent feature vectors.
 4. Themethod of claim 2, further comprising optimizing the polygonal mesh overthe second set of latent feature vectors using a photometric objectivefunction.
 5. The method of claim 4, wherein optimizing the polygonalmesh includes: selecting a pair of consecutive frames from an imagesequence; creating a virtual viewpoint for the selected pair ofconsecutive frames; calculating photometric error as a differencebetween pixel intensities sampled from both frames of the pair ofconsecutive frames; and backpropagating a gradient of the photometricerror into vertices of the polygonal mesh.
 6. The method of claim 5,wherein the photometric objective function includes applying aparameterized warp function to pixels in at least two images of theimage sequence.
 7. The method of claim 5, wherein the virtual viewpointaccounts for multi-view geometry associated with first and second cameraviewpoints, the method further comprising: rasterizing the polygonalmesh to obtain a depth map from the virtual viewpoint; aligning therasterized polygonal mesh by projecting a set of three-dimensional (3D)points from the depth map to each of the first and second cameraviewpoints; and sampling one or more pixel intensities from the alignedrasterized polygonal mesh.
 8. A computer program product including oneor more non-transitory machine-readable mediums having instructionsencoded thereon that when executed by at least one processor causes aprocess to be carried out for 3D object reconstruction using photometricmesh representations, the process comprising: transforming a set oftwo-dimensional (2D) data points representing one or more shape priorsinto a first set of latent feature vectors representing a shape of afirst object; generating a reconstructed representation of the firstobject in three dimensions based on the first set of latent featurevectors and a second set of latent feature vectors representing apre-defined shape of a second object using a photometric objectivefunction; and causing the reconstructed representation of the firstobject to be output to an output device.
 9. The computer program productof claim 8, wherein the process further comprises generating a polygonalmesh representing the shape of the first object using an object meshgeneration neural network trained to transform the set oftwo-dimensional (2D) data points representing the first object into thepolygonal mesh.
 10. The computer program product of claim 9, whereingenerating the polygonal mesh includes: selecting an image of the firstobject from an image sequence; generating the first set of latentfeature vectors by feeding the selected image of the first object intoan encoder of the neural network; and rendering the polygonal mesh basedon the first set of latent feature vectors.
 11. The computer programproduct of claim 9, wherein the process further comprises optimizing thepolygonal mesh over the second set of latent feature vectors using thephotometric objective function.
 12. The computer program product ofclaim 11, wherein optimizing the polygonal mesh includes: selecting apair of consecutive frames from an image sequence; creating a virtualviewpoint for the selected pair of consecutive frames, the virtualviewpoint accounting for multi-view geometry associated with first andsecond camera viewpoints; rasterizing the polygonal mesh to obtain adepth map from the virtual viewpoint; aligning the rasterized polygonalmesh by projecting a set of three-dimensional (3D) points from the depthmap to each of the first and second camera viewpoints; sampling one ormore pixel intensities from the aligned rasterized polygonal mesh; andbackpropagating gradients from a difference between the one or moresampled pixel intensities into vertices of the polygonal mesh.
 13. Thecomputer program product of claim 12, wherein the photometric objectivefunction includes applying a parameterized warp function to pixels in atleast two images of the image sequence.
 14. The computer program productof claim 12, wherein backpropagating gradients from a difference betweenthe one or more sampled pixel intensities into vertices of the polygonalmesh includes backpropagating a gradient of the photometric error intovertices of the polygonal mesh, and wherein the process furthercomprises calculating the photometric error as the difference betweenpixel intensities sampled from both frames of the pair of consecutiveframes.
 15. A system for 3D object reconstruction using photometric meshrepresentations, the system comprising: a means for generating apolygonal mesh representing a shape of a first object; a means foroptimizing the polygonal mesh over a set of latent feature vectors usinga photometric objective function to produce a reconstructedrepresentation of the first object in three dimensions, the set oflatent feature vectors representing a pre-defined shape of a secondobject; and a means for causing the reconstructed representation of thefirst object to be at least one of displayed or printed.
 16. The systemof claim 15, wherein the means for generating a polygonal mesh isconfigured to generate the polygonal mesh using an object meshgeneration neural network trained to transform a set of two-dimensional(2D) data points representing the first object into the polygonal mesh,the set of 2D data points representing color pixels in at least twoimages of the first object, the at least two images having differentcamera poses.
 17. The system of claim 16, further comprising a means fortraining the object mesh generation neural network to transform the setof 2D data points into the polygonal mesh using 3D computer aideddrawing (CAD) model renderings.
 18. The system of claim 15, wherein thephotometric objective function represents, at least in part, aphotometric loss contributed by pixels in at least one face of thepolygonal mesh.
 19. The system of claim 15, wherein the photometricobjective function is:${\mathcal{L}_{phot}^{(j)}\left( V_{j} \right)} = {\sum\limits_{{i\text{:}x_{i}} \in \mathcal{X}_{j}}{{{\mathcal{I}_{S}\left( x_{i}^{\prime} \right)} - {\mathcal{I}_{T}\left( x_{i} \right)}}}_{1}}$where x′_(i)=W(x_(i); V_(j)) is a warped pixel coordinate x_(i) in apolygonal mesh V of a first image I_(S) of the first object, I_(T) is asecond image of the first object, V is the polygonal mesh, and χ_(j) isa set of visible pixel coordinates within a projection range of the atleast one face j of the polygonal mesh.